DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 10/31/2023 have been considered by the examiner.
Priority
Receipt is acknowledged of certified copies of papers submitted under 35 U.S.C. 119(a)-(d), of which papers have been placed in the file wrapper.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION. —The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 5, 9, and 16 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 5 recites the limitation, “wherein the carbon sequestration technique.”
There is insufficient antecedent basis for this limitation in the claim, when it depends from claim 2. It appears that the claim should instead depend from claim 4.
Claim 9 recites the limitation, “the look up table.” There is insufficient antecedent basis for this limitation in the claim, when it depends from claim 1. It appears that the claim should instead depend from claim 8.
Claim 16 recites the limitation, “wherein the tillage score is determined based on the soil texture, the soil color, the line density in furrow direction, and the tillage weight.” There is insufficient antecedent basis for this limitation in the claim, when it depends from claim 14. It appears that the claim should instead depend from claim 15.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite a method for calculating a net carbon flux and carbon footprint of at least one agriculture crop.
The limitations of the claims, use mathematical distances and functions to compute a net carbon flux and a carbon footprint. The claim limitations under their broadest reasonable interpretation, cover mathematical concepts and calculations.
The limitations recite a mathematical calculations. The grouping of “mathematical concepts’ in the 2019 PEG includes “mathematical calculations” as an exemplar of an abstract idea. 2019 PEG Section I, 84 Fed. Reg. at 52. Thus, limitation (a) falls into the “mathematical concept” grouping of abstract ideas
This judicial exception is not integrated into a practical application. While determining the net carbon sequestration may provide useful data, there is no further description as to how this is integrated into a practical application.
In particular, independent claims 1, 14, and 20 only recite one additional element — using a processor to process an image and make computations. The processor in both steps is recited at a high-level of generality (i.e., as a generic processor performing a generic computer) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a processor to compute a carbon flux and carbon footprint amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim is not patent eligible
Note: no prior art rejection has been provided for claims 2-3, 5-10, 15-16, and 18, these claims would potentially be objected to as containing allowable subject matter, if the 101 abstract idea rejection and the 112(b)-indefiniteness rejection are overcome.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1, 11-14, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Ashtekar et al. (US Pub. No. 2022/0138649 A1) in view of Turner et al. (“Monitoring Forest Carbon Sequestration with Remote Sensing and Carbon Cycle Modeling”).
Regarding claim 1, Ashtekar discloses, a processor implemented method to calculate net carbon sequestration for agriculture (See Ashtekar ¶43, “server 130 may access truth-based data, public data, commercial data, and scientific data in order to translate this data into carbon footprints and carbon sequestration.”)
using remote sensing data, the method comprising: (See Ashtekar ¶45, “satellite and aerial image data taken across agriculturally meaningful spectral bands (e.g., LANDSAT, SENTINEL) that may be processed by the CO2E sequestration server 130.”)
processing via one or more hardware processors, remote sensing data comprising one or more input images (See Ashtekar ¶53, “The remote sense processor 156 may process satellite/aerial images.”)
indicating one or more characteristics of at least one agriculture crop associated with a geographical region; (See Ashtekar ¶45, “satellite and aerial image data taken across agriculturally meaningful spectral bands (e.g., LANDSAT, SENTINEL) that may be processed by the CO2E sequestration server 130 to understand crop types, rotations, baseline management practices (e.g., planting dates, tillage types and dates, fertilization types and dates, irrigation types and dates, harvesting dates), and stages of growth at any given time.”)
calculating via the one or more hardware processors, a carbon footprint value of at least one agriculture crop by obtaining a plurality of carbon values associated with the geographical region, (See Ashtekar ¶43, “Though represented in the block diagram as single databases 121-124, each of the databases 121-124 may comprise a substantial number of databases through which the CO2E sequestration server 130 may access truth-based data, public data, commercial data, and scientific data in order to translate this data into carbon footprints and carbon sequestration resulting from regenerative management practices into agriculturally meaningful metrics and valuations for a vast number of agricultural parcels.”)
wherein the plurality of carbon values includes an intensive tillage value from soil carbon release, (See Ashtekar ¶123, “At block 708, the CO2E determination processor 155 is configured to infer tillage practices on a field, such as tillage type and tillage date. This is done by monitoring an index (e.g., NDTI) over a period of time. Different tillage practices lead to different amounts of residue on the field surface, so by observing the changes in NDTI over time the CO2E determination processor 155 can infer which tillage practices have been employed and when corresponding tillage events occurred.”)
a carbon sequestered in soil value, (See Ashtekar ¶36, “Carbon Sequestration: The amount of additional carbon is retained in the soil. In some cases, the amount of carbon in the soil increases over time and such is referred to as the amount of carbon that is being sequestered.” Further see Ashtekar ¶55, “The results of the crop simulations and remotely sensed images may be employed by the CO2E detection processor 155 to determine the carbon sequestration potential for parcels in the parcel database 151.”)
one or more agriculture crop management practices, (See Ashtekar ¶54, The CO2E management practices processor 154 may further access data from the databases 121-124 to determine one or more regenerative management practices (e.g., crop species and maturity; planting dates; crop rotation; cover cropping; tillage type; fertilizer type, amount, and timing; and irrigation amount and timing), where the one or more regenerative management practices are employed to construct simulation inputs to the crop simulation processor 153 for modeling of regenerative multi-year crop simulations in order to accurately determine the amount of carbon that may be sequestered over baseline field management.”)
and a locked carbon value above ground crop biomass; (See Ashtekar ¶80, “Thus, according to the inputs provided by block 308, crop simulations are run at scale by the crop simulation processor 153 to generate components (e.g., CO2 flux from the soil, N2O flux from the soil, CO2 from tractor fuel use, CO2 from production of nitrogen fertilizer, etc.) from which greenhouse gas emissions in units of CO2E are calculated, parcel yields per planting season along with a number of other corresponding simulation outputs such as, but not limited to, plant growth stage, plant leaf area, solar energy absorbed through the leaves, biomass accumulated in different plant tissues.”)
and calculating via the one or more hardware processors, a net carbon flux of least one agriculture crop (See Ashtekar ¶105, “At block 606, the CO2E determination processor 155 retrieves outputs corresponding to carbon dioxide flux from the soil (one component of the greenhouse gas emissions for each parcel) from the parcel database 151 and calculates the carbon dioxide flux from the soil by taking the difference in total soil organic carbon between the ends of the first year and last year simulations.”)
based on the carbon footprint value, (See Ashtekar ¶137, “At block 806, the CO2E sequestration server 130 executes the carbon footprint determination flow of FIG. 6 to determine that carbon sequestration potential for the parcel.”)
a data maturity index, (See Ashtekar ¶100, “At block 508, relevant spectral bands for a given observation are combined to generate composite vegetative indices for subparts of the parcels according to well-known techniques. Preferably, the Landsat Surface Reflectance-derived Enhanced Vegetation Index (EVI) is employed to determine crop type and maturity.”)
and a difficulty level. (See Ashtekar ¶122, “The CO2E determination processor 155 is configured to infer what crop is growing in a particular field, and when that crop emerged from the ground. This is done by monitoring a vegetative index (such as EVI or NDVI) over time. Different crops have different vegetative index curves, so by observing the increases in EVI/NDVI over time, the CO2E determination processor 155 can infer what crop is growing and when the crop was planted.”)
Ashtekar discloses the above limitations but he fails to disclose, an agricultural crop respiration loss value.
However, Turner discloses, an agricultural crop respiration loss value, (See Turner p. 457 right col 2nd para, “NEP (net ecosystem production) is the net effect of photosynthetic carbon uptake and release of carbon to the atmosphere from respiration by autotrophs (plants) and heterotrophs.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the crop respiration to calculate net carbon production as suggested by Turner to Ashtekar’s calculation of net carbon flux and carbon footprint. This can be done using known engineering techniques, with a reasonable expectation of success. The motivation for doing so is because accurately accounting for crop respiration allows for better management of net emissions, assessment of soil health, and identification of opportunities to improve crop yield by reducing non-essential respiratory carbon loss.
Regarding claim 11, Ashtekar and Turner disclose, the processor implemented method as claimed in claim 1, wherein the net carbon sequestration of the crop is a total sum of the carbon footprint value, the data maturity index, and the difficulty level. crop (See Ashtekar ¶137, “At block 806, the CO2E sequestration server 130 executes the carbon footprint determination flow of FIG. 6 to determine that carbon sequestration potential for the parcel.”
Further see Ashtekar ¶100, “At block 508, relevant spectral bands for a given observation are combined to generate composite vegetative indices for subparts of the parcels according to well-known techniques. Preferably, the Landsat Surface Reflectance-derived Enhanced Vegetation Index (EVI) is employed to determine crop type and maturity.”)
Further see Ashtekar ¶122, “The CO2E determination processor 155 is configured to infer what crop is growing in a particular field, and when that crop emerged from the ground. This is done by monitoring a vegetative index (such as EVI or NDVI) over time. Different crops have different vegetative index curves, so by observing the increases in EVI/NDVI over time, the CO2E determination processor 155 can infer what crop is growing and when the crop was planted.”)
Regarding claim 12, Ashtekar and Turner disclose, the processor implemented method as claimed in claim 1, wherein the data maturity index is one or more weightage maturity indices associated with one or more crop parameters to assess crop maturity. (See Ashtekar ¶100, “At block 508, relevant spectral bands for a given observation are combined to generate composite vegetative indices for subparts of the parcels according to well-known techniques. Preferably, the Landsat Surface Reflectance-derived Enhanced Vegetation Index (EVI) is employed to determine crop type and maturity.”)
Regarding claim 13, Ashtekar and Turner disclose, the processor implemented method as claimed in claim 1, wherein the difficulty level is a categorical rate of the carbon value obtained from the agriculture crop. (See Ashtekar ¶122, “The CO2E determination processor 155 is configured to infer what crop is growing in a particular field, and when that crop emerged from the ground. This is done by monitoring a vegetative index (such as EVI or NDVI) over time. Different crops have different vegetative index curves, so by observing the increases in EVI/NDVI over time, the CO2E determination processor 155 can infer what crop is growing and when the crop was planted.”)
Regarding claim 14, Ashtekar discloses, a system 100 to calculate net carbon sequestration for agriculture (See Ashtekar ¶43, “server 130 may access truth-based data, public data, commercial data, and scientific data in order to translate this data into carbon footprints and carbon sequestration.”)
using remote sensing data comprising: (See Ashtekar ¶45, “satellite and aerial image data taken across agriculturally meaningful spectral bands (e.g., LANDSAT, SENTINEL) that may be processed by the CO2E sequestration server 130.”)
a memory (102) storing instructions; (See Ashtekar ¶154, “The memory 1006 may include an operating system 1007 such as, but not limited to, Microsoft Windows, Mac OS, Unix, and Linux, where the operating system 1007 is configured to manage execution by the CPU 1001 of program instructions that are components of one or more application programs.”)
one or more communication interfaces (106); and one or more hardware processors (104) coupled to the memory (102) via the one or more communication interfaces (106), (See Ashtekar ¶152, “The CO2E sequestration server 1000 may include one or more central processing units (CPU) 1001 that are coupled to memory 1006 having both transitory and non-transitory memory components therein. The CPU 1001 is also coupled to a communications circuit 1002.”)
wherein the one or more hardware processors (104) are configured by the instructions to: process remote sensing data comprising one or more input images (See Ashtekar ¶53, “The remote sense processor 156 may process satellite/aerial images.”)
indicating one or more characteristics of at least one agriculture crop associated with a geographical region; (See Ashtekar ¶45, “satellite and aerial image data taken across agriculturally meaningful spectral bands (e.g., LANDSAT, SENTINEL) that may be processed by the CO2E sequestration server 130 to understand crop types, rotations, baseline management practices (e.g., planting dates, tillage types and dates, fertilization types and dates, irrigation types and dates, harvesting dates), and stages of growth at any given time.”)
calculate a carbon footprint value of at least one agriculture crop by obtaining a plurality of carbon values associated with the geographical region, (See Ashtekar ¶43, “Though represented in the block diagram as single databases 121-124, each of the databases 121-124 may comprise a substantial number of databases through which the CO2E sequestration server 130 may access truth-based data, public data, commercial data, and scientific data in order to translate this data into carbon footprints and carbon sequestration resulting from regenerative management practices into agriculturally meaningful metrics and valuations for a vast number of agricultural parcels.”)
wherein the plurality of carbon values includes an intensive tillage value from soil carbon release, (See Ashtekar ¶123, “At block 708, the CO2E determination processor 155 is configured to infer tillage practices on a field, such as tillage type and tillage date. This is done by monitoring an index (e.g., NDTI) over a period of time. Different tillage practices lead to different amounts of residue on the field surface, so by observing the changes in NDTI over time the CO2E determination processor 155 can infer which tillage practices have been employed and when corresponding tillage events occurred.”)
a carbon sequestered in soil value, (See Ashtekar ¶36, “Carbon Sequestration: The amount of additional carbon is retained in the soil. In some cases, the amount of carbon in the soil increases over time and such is referred to as the amount of carbon that is being sequestered.” Further see Ashtekar ¶55, “The results of the crop simulations and remotely sensed images may be employed by the CO2E detection processor 155 to determine the carbon sequestration potential for parcels in the parcel database 151.”)
one or more agriculture crop management practices, (See Ashtekar ¶54, The CO2E management practices processor 154 may further access data from the databases 121-124 to determine one or more regenerative management practices (e.g., crop species and maturity; planting dates; crop rotation; cover cropping; tillage type; fertilizer type, amount, and timing; and irrigation amount and timing), where the one or more regenerative management practices are employed to construct simulation inputs to the crop simulation processor 153 for modeling of regenerative multi-year crop simulations in order to accurately determine the amount of carbon that may be sequestered over baseline field management.”)
and a locked carbon value above ground crop biomass; (See Ashtekar ¶80, “Thus, according to the inputs provided by block 308, crop simulations are run at scale by the crop simulation processor 153 to generate components (e.g., CO2 flux from the soil, N2O flux from the soil, CO2 from tractor fuel use, CO2 from production of nitrogen fertilizer, etc.) from which greenhouse gas emissions in units of CO2E are calculated, parcel yields per planting season along with a number of other corresponding simulation outputs such as, but not limited to, plant growth stage, plant leaf area, solar energy absorbed through the leaves, biomass accumulated in different plant tissues.”)
and calculate a net carbon flux of least one agriculture crop (See Ashtekar ¶105, “At block 606, the CO2E determination processor 155 retrieves outputs corresponding to carbon dioxide flux from the soil (one component of the greenhouse gas emissions for each parcel) from the parcel database 151 and calculates the carbon dioxide flux from the soil by taking the difference in total soil organic carbon between the ends of the first year and last year simulations.”)
based on the carbon footprint value, (See Ashtekar ¶137, “At block 806, the CO2E sequestration server 130 executes the carbon footprint determination flow of FIG. 6 to determine that carbon sequestration potential for the parcel.”)
a data maturity index, (See Ashtekar ¶100, “At block 508, relevant spectral bands for a given observation are combined to generate composite vegetative indices for subparts of the parcels according to well-known techniques. Preferably, the Landsat Surface Reflectance-derived Enhanced Vegetation Index (EVI) is employed to determine crop type and maturity.”)
and a difficulty level. (See Ashtekar ¶122, “The CO2E determination processor 155 is configured to infer what crop is growing in a particular field, and when that crop emerged from the ground. This is done by monitoring a vegetative index (such as EVI or NDVI) over time. Different crops have different vegetative index curves, so by observing the increases in EVI/NDVI over time, the CO2E determination processor 155 can infer what crop is growing and when the crop was planted.”)
Ashtekar discloses the above limitations but he fails to disclose, an agricultural crop respiration loss value.
However, Turner discloses, an agricultural crop respiration loss value, (See Turner p. 457 right col 2nd para, “NEP (net ecosystem production) is the net effect of photosynthetic carbon uptake and release of carbon to the atmosphere from respiration by autotrophs (plants) and heterotrophs.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the crop respiration to calculate net carbon production as suggested by Turner to Ashtekar’s calculation of net carbon flux and carbon footprint. This can be done using known engineering techniques, with a reasonable expectation of success. The motivation for doing so is because accurately accounting for crop respiration allows for better management of net emissions, assessment of soil health, and identification of opportunities to improve crop yield by reducing non-essential respiratory carbon loss.
Regarding claim 20, Ashtekar discloses, one or more non-transitory machine-readable information storage mediums (See Ashtekar ¶152, “The CO2E sequestration server 1000 may include one or more central processing units (CPU) 1001 that are coupled to memory 1006 having both transitory and non-transitory memory components therein.”)
comprising one or more instructions which when executed by one or more hardware processors cause: (See Ashtekar ¶154, “where the operating system 1007 is configured to manage execution by the CPU 1001 of program instructions that are components of one or more application programs.”)
processing remote sensing data comprising one or more input images (See Ashtekar ¶53, “The remote sense processor 156 may process satellite/aerial images.”)
indicating one or more characteristics of at least one agriculture crop associated with a geographical region; (See Ashtekar ¶45, “satellite and aerial image data taken across agriculturally meaningful spectral bands (e.g., LANDSAT, SENTINEL) that may be processed by the CO2E sequestration server 130 to understand crop types, rotations, baseline management practices (e.g., planting dates, tillage types and dates, fertilization types and dates, irrigation types and dates, harvesting dates), and stages of growth at any given time.”)
calculating a carbon footprint value of at least one agriculture crop by obtaining a plurality of carbon values associated with the geographical region, (See Ashtekar ¶43, “Though represented in the block diagram as single databases 121-124, each of the databases 121-124 may comprise a substantial number of databases through which the CO2E sequestration server 130 may access truth-based data, public data, commercial data, and scientific data in order to translate this data into carbon footprints and carbon sequestration resulting from regenerative management practices into agriculturally meaningful metrics and valuations for a vast number of agricultural parcels.”)
wherein the plurality of carbon values includes an intensive tillage value from soil carbon release, (See Ashtekar ¶123, “At block 708, the CO2E determination processor 155 is configured to infer tillage practices on a field, such as tillage type and tillage date. This is done by monitoring an index (e.g., NDTI) over a period of time. Different tillage practices lead to different amounts of residue on the field surface, so by observing the changes in NDTI over time the CO2E determination processor 155 can infer which tillage practices have been employed and when corresponding tillage events occurred.”)
a carbon sequestered in soil value, (See Ashtekar ¶36, “Carbon Sequestration: The amount of additional carbon is retained in the soil. In some cases, the amount of carbon in the soil increases over time and such is referred to as the amount of carbon that is being sequestered.” Further see Ashtekar ¶55, “The results of the crop simulations and remotely sensed images may be employed by the CO2E detection processor 155 to determine the carbon sequestration potential for parcels in the parcel database 151.”)
one or more agriculture crop management practices, (See Ashtekar ¶54, The CO2E management practices processor 154 may further access data from the databases 121-124 to determine one or more regenerative management practices (e.g., crop species and maturity; planting dates; crop rotation; cover cropping; tillage type; fertilizer type, amount, and timing; and irrigation amount and timing), where the one or more regenerative management practices are employed to construct simulation inputs to the crop simulation processor 153 for modeling of regenerative multi-year crop simulations in order to accurately determine the amount of carbon that may be sequestered over baseline field management.”)
and a locked carbon value above ground crop biomass; (See Ashtekar ¶80, “Thus, according to the inputs provided by block 308, crop simulations are run at scale by the crop simulation processor 153 to generate components (e.g., CO2 flux from the soil, N2O flux from the soil, CO2 from tractor fuel use, CO2 from production of nitrogen fertilizer, etc.) from which greenhouse gas emissions in units of CO2E are calculated, parcel yields per planting season along with a number of other corresponding simulation outputs such as, but not limited to, plant growth stage, plant leaf area, solar energy absorbed through the leaves, biomass accumulated in different plant tissues.”)
and calculating a net carbon flux of least one agriculture crop (See Ashtekar ¶105, “At block 606, the CO2E determination processor 155 retrieves outputs corresponding to carbon dioxide flux from the soil (one component of the greenhouse gas emissions for each parcel) from the parcel database 151 and calculates the carbon dioxide flux from the soil by taking the difference in total soil organic carbon between the ends of the first year and last year simulations.”)
based on the carbon footprint value, (See Ashtekar ¶137, “At block 806, the CO2E sequestration server 130 executes the carbon footprint determination flow of FIG. 6 to determine that carbon sequestration potential for the parcel.”)
a data maturity index, (See Ashtekar ¶100, “At block 508, relevant spectral bands for a given observation are combined to generate composite vegetative indices for subparts of the parcels according to well-known techniques. Preferably, the Landsat Surface Reflectance-derived Enhanced Vegetation Index (EVI) is employed to determine crop type and maturity.”)
and a difficulty level. (See Ashtekar ¶122, “The CO2E determination processor 155 is configured to infer what crop is growing in a particular field, and when that crop emerged from the ground. This is done by monitoring a vegetative index (such as EVI or NDVI) over time. Different crops have different vegetative index curves, so by observing the increases in EVI/NDVI over time, the CO2E determination processor 155 can infer what crop is growing and when the crop was planted.”)
Ashtekar discloses the above limitations but he fails to disclose, an agricultural crop respiration loss value.
However, Turner discloses, an agricultural crop respiration loss value, (See Turner p. 457 right col 2nd para, “NEP (net ecosystem production) is the net effect of photosynthetic carbon uptake and release of carbon to the atmosphere from respiration by autotrophs (plants) and heterotrophs.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the crop respiration to calculate net carbon production as suggested by Turner to Ashtekar’s calculation of net carbon flux and carbon footprint. This can be done using known engineering techniques, with a reasonable expectation of success. The motivation for doing so is because accurately accounting for crop respiration allows for better management of net emissions, assessment of soil health, and identification of opportunities to improve crop yield by reducing non-essential respiratory carbon loss.
Claims 4 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Ashtekar et al. (US Pub. No. 2022/0138649 A1) in view of Turner et al. (“Monitoring Forest Carbon Sequestration with Remote Sensing and Carbon Cycle Modeling”) and in further view of Stolt et al. (US Pub. No. 2024/0354851 A1).
Regarding claim 4, Ashtekar and Turner disclose, the processor implemented method as claimed in claim 1, but they fail to disclose, wherein the carbon sequestered in soil value is obtained by, acquiring the one or more input images indicating agriculture crop characteristics associated with the geographical region; and determining by using a pretrained carbon sequestration technique, the soil carbon sequestered value based on a carbon difference between the one or more input images indicating agriculture crop and a training dataset.
However, Stolt discloses, wherein the carbon sequestered in soil value is obtained by, acquiring the one or more input images indicating agriculture crop characteristics associated with the geographical region; and determining by using a pretrained carbon sequestration technique, the soil carbon sequestered value based on a carbon difference between the one or more input images indicating agriculture crop and a training dataset. (See Stolt ¶45, “The method may involve using a machine learning model trained to translate changes in time of satellite images to sequestrate carbon dioxide. The process of training such a model may be based on other technologies and real ground measurements. It is also possible to integrate detailed land cover information with ground observations of forest inventories. In one embodiment the method comprises the step of analyzing the satellite images using a machine learning model trained to translate changes in time of satellite images to sequestrated carbon dioxide. The machine learning model may be trained to translate changes in time of satellite images to sequestrated carbon dioxide for a specific geographic area.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the obtaining carbon sequestration using a machine learning model as suggested by Stolt to Ashtekar and Turner’s determination of a carbon sequestration value. This can be using known engineering techniques, with a reasonable expectation of success. The motivation for doing so is because pretrained machine learning models can capture non-linear relationships between crop growth, climate factors, and soil carbon to achieve higher precision than single-date imagery.
Regarding claim 17, Ashtekar, Turner, and Stolt disclose, the system as claim in claim 14, wherein the carbon sequestered in soil value is obtained by, acquiring the one or more input images indicating agriculture crop characteristics associated with the geographical region; and determining by using a pretrained carbon sequestration technique, the soil carbon sequestered value based on a carbon difference between the one or more input images indicating agriculture crop and a training dataset. (See the rejection of claim 4 as it is equally applicable for claim 17 as well.)
Conclusion
Listed below are the prior arts made of record and not relied upon but are considered pertinent to applicant’s disclosure.
Adams (US Pub. No. 2022/0148305 A1) A computer implemented method includes obtaining geospatial coordinates for multiple points defining boundaries of a subject property, obtaining an image of the subject property from one or more positions above the earth, identifying forms and amounts of vegetation within the boundaries of the subject property based on the image, determining carbon offset values for the forms of vegetation identified within the boundaries of the subject property, and combining the carbon offset values based on the amounts of vegetation to derive a total offset for the subject property. Carbon offset values may be determined for multiple properties and aggregated until a threshold total value is reached, forming an aggregated carbon offset. An electronic exchange may be updated with the aggregated carbon offset. Value received may be apportioned back to respective property owners.
Guan et al. (US Pub. No. 2025/0173749 A1) A methodology is used to quantify implications and/or footprints of carbon, water, and/or nutrients of a particular crop in a region on a large scale and at field-level. A methodology is used to quantify, calculate, and/or visualize cover crop traits, tillage practices, and/or their outcomes at large scale. A methodology is used to accurately derive, estimate, and/or predict large-scale, long-term, and field-level cover crop adoption and biomass information using remote sensing time series.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DAVID PERLMAN whose telephone number is (571) 270-1417.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Chineyere Wills-Burns can be reached at (571) 272-9752. The fax phone number for the organization where this application or proceeding is assigned is
(571) 273-8300.
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/DAVID PERLMAN/Primary Examiner, Art Unit 2673
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